The problem of identifying a specific design or architecture that allows to satisfy all the system requirements becomes more difficult when uncertainties are taken into account. When a requirement is subject to uncertainty there are a number approaches available to the system engineer, each one with its own benefits and disadvantages. Classical robust optimization is one of the attractive approaches for optimization under uncertainty, since it allows to select the design that will perform better in the worst case scenario. In this framework uncertainty is described deterministically by uncertainty sets, which directly impact characteristics of the robust counterpart problem and quality of the robust solution. In other words, depending on the way uncertainty sets are defined, there is a chance that the problem becomes infeasible, meaning that no architecture satisfies all the system requirements in presence of uncertainty. This paper shows the effect of uncertainties on the feasibility of the problem introducing a measure of infeasibility.
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